Local Gaussian process extrapolation for BART models with applications to causal inference

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)724-735
Journal / PublicationJournal of Computational and Graphical Statistics
Volume33
Issue number2
Online published26 Jul 2023
Publication statusPublished - 2024

Abstract

Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard implementations of BART typically suffer from inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data. This paper proposes a novel extrapolation strategy that grafts Gaussian processes to the leaf nodes in BART for predicting points outside the range of the observed data. The new method is compared to standard BART implementations and recent frequentist resampling-based methods for predictive inference. We apply the new approach to a challenging problem from causal inference, wherein for some regions of predictor space, only treated or untreated units are observed (but not both). In simulation studies, the new approach boasts superior performance compared to popular alternatives, such as Jackknife+. © 2023 American Statistical Association and Institute of Mathematical Statistics

Research Area(s)

  • Tree, Extrapolation, Gaussian process, Predictive interval, XBART, XBCF

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).